Systems and methods for look-alike sound-alike medication error messaging

ABSTRACT

Systems and methods are provided for look-alike sound-alike medication error messaging. Prescription data relating to a prescription is parsed to identify a submitted drug product and a submitted daily dosage. An absolute dose screening process may be executed to determine whether the submitted daily dosage meets absolute dosing criteria for the submitted drug product. A typical dose screening process may be executed to determine whether the submitted daily dosage meets statistically derived typical dosing criteria for the submitted drug product and any look-alike sound-alike alternative drug products. In addition, one or more likelihood indicators may be assigned to the submitted drug product in relation to associated look-alike sound-alike drug pairs. If it is determined that the prescription should be rejected based on typical dosing criteria, absolute dosing criteria, or the likelihood indicator(s), a reject message may be built for presentation to the pharmacist.

RELATED APPLICATIONS

The present application claims the benefit of U.S. Provisional PatentApplication Ser. No. 60/413,563 filed Sep. 25, 2002, which is herebyincorporated by reference as if set forth fully herein.

TECHNICAL FIELD

The present invention relates generally to medication errors involvingmedication names that look and/or sound alike. More particularly, thepresent invention relates to systems and methods for intelligentlydetecting look-alike sound-alike medication errors within prescriptiontransactions and the like.

BACKGROUND OF THE INVENTION

Medication errors are increasingly recognized as an important cause ofpreventable deaths and injuries. A significant percentage of medicationerrors occur when a prescribed medication is confused with anon-prescribed medication and the non-prescribed medication is dispensedto the patient. Medication brand names can look like other brand nameswhen handwritten or may be mistaken for another drug when orderedorally. Generic medication names can resemble other generic medicationnames or even brand names. Medication errors can also occur when thelabeling or packaging of multiple drugs is too similar. Medicationerrors resulting from such confusion between drugs are often referred toas look-alike sound-alike (“LASA”) medication errors.

Millions of dollars may be spent establishing a brand name well before adrug is ever introduced to the market. Thus, drug manufacturers areextremely reluctant to change medication brand names. Changing a genericdrug name can also be a complicated and expensive undertaking. Such amodification would affect all the companies that manufacture thecompound, not to mention the numerous text references and softwareprograms that refer to the generic drug name. Generic drug names may bebased on word stems related to particular drug class, a factor thatcauses much overlap between generic drug names.

Given the resistance to change a medication name, efforts have been madeto anticipate and avoid LASA medication errors before a medication nameis adopted. As one example, special software has been developed toscreen proposed medication names against databases of existingmedication names. The software computes a numerical similarity scorebetween the proposed drug name and other drug names. The proposed drugname is measured for its resemblance to all of the drug names stored ina massive database of medication brand and generic names.

Even with pre-screening techniques, LASA errors continue to occur. Shortof a medication name change, alert systems are used to alert pharmacistsof potential LASA errors. Such systems generate a warning message anytime a drug product having a drug name that is included in a LASA drugpair is detected in a prescription transaction. The term “LASA drugpair,” as used herein, refers to two or more drug names that are knownto be confused with each other. Each member of a LASA drug pair can bereferred to as a LASA alternative drug name to the other member(s).Systems that generate warning messages any time a drug product having adrug name that is included in a LASA drug pair is detected can generatea high volume of messages, the majority of which are “false positives.”As a result, such warning messages tend to be more of a burden to busypharmacists than an aide.

It is clear that existing pharmacy decision support and practicemanagement systems do not adequately protect against LASA medicationerrors. What is needed is a system and method for intelligentlydetecting LASA medication errors based on more than simply whether adrug name is included in a LASA drug pair. The sensitivity of such asystem and method should be adjustable, so as to provide the ability toincrease or decrease the rate of LASA medication error messaging. Thereis further a need for a system and method that monitors prescriptiontransactions for possible LASA medication errors and generates messageswhen there is a likelihood that a different medication, dispensequantity, or days supply is more appropriate.

SUMMARY OF THE INVENTION

The present invention provides systems and methods for look-alikesound-alike medication error messaging. A submitted drug product and asubmitted daily dosage for a prescription are identified fromprescription data, such as that which is included in a prescriptiontransaction or the like. If the submitted drug product is associatedwith at least one look-alike sound-alike drug pair comprising at leastone look-alike sound-alike alternative drug name, determinations may bemade as to whether the submitted daily dosage meets pre-determinedstatistically derived typical dosing criteria and/or absolute dosingcriteria. In addition, a likelihood indicator may be determined for thesubmitted drug product. Likelihood indicators may be used to quantifythe relative probability of whether a drug product is involved in a LASAmedication error.

Determining whether the submitted daily dosage meets pre-determinedtypical dosing criteria may involve determining whether the submitteddaily dosage is typical or atypical for the submitted drug productand/or for any LASA alternative drug products associated with any LASAalternative drug name. If the submitted daily dosage does not meet thestatistically derived typical dosing criteria for the submitted drugproduct, a typical dose message may be determined for the prescription.The statistically derived typical dosing criteria may optionally bespecific to patient demographic group, treatment type, illness type orphysician specialty. A clinical significance may also be determined forthe look-alike sound-alike drug pair. Clinical significance may be avalue used to quantify the consequences of a look-alike sound-alikemedication error involving the look-alike sound-alike drug pair. Atypical dose edit action may be determined based on the clinicalsignificance of the look-alike sound-alike drug pair. The typical doseedit action may further be determined based on whether the prescriptionrelates to a new prescription or a refill. The typical dose edit actionmay be used to indicate whether the prescription should be rejected.

Determining whether the submitted daily dosage meets pre-determinedabsolute dosing criteria may involve determining whether the submitteddaily dosage exceeds an absolute maximum daily dosage or is less than anabsolute minimum daily dosage for the submitted drug product. If thesubmitted daily dosage does not meet the absolute dosing criteria forthe submitted drug product, an absolute dose message may be determinedfor the prescription. The absolute dose edit action may also bedetermined based on whether the prescription relates to a newprescription or a refill. The absolute dose edit action may be used toindicate whether the prescription should be rejected. Absolute dosingcriteria may optionally be specific to patient demographic group,treatment type and illness type.

Likelihood indicators may be stored in a database and may bepre-determined based on factors such as a degree of similarity betweenlook-alike sound alike drug names, prescribing frequencies assigned tothe drug product associated with the look-alike sound-alike drug pair,and the availability of associated drug products in same, look-alike orsound-alike strengths, as well as other likelihood enhancing ordiminishing factors. Degree of similarity may be computed as theLevenshtein Distance between the drug names of the submitted drugproduct and the look-alike sound-alike alternative drug product.Prescribing frequencies may be categorized as being either high, mediumor low. Low-low, high-low or low-high combinations of prescribingfrequencies for the submitted drug product and the look-alikesound-alike alternative drug product may be considered to have thepotential for confirmation bias. A likelihood edit action may bedetermined based on the likelihood indicator and whether theprescription relates to a new or refill prescription. The likelihoodedit action may be used to indicate whether the prescription should berejected. A likelihood message may be determined based on the likelihoodedit action.

If at least one of the typical dose edit action, the absolute dose editaction and the likelihood edit action indicates that the prescriptionshould be rejected, a reject message may be built and presented to thepharmacist. The reject message may include the absolute dose message ifthe absolute dose edit action indicates that the prescription should berejected, the typical dose message if the typical dose edit actionindicates that the prescription should be rejected, and the likelihoodmessage if the likelihood edit action indicates that the prescriptionshould be rejected. Inclusion of more than one of the absolute dosemessage, the typical dose message and the likelihood message in thereject message is dependent on there being sufficient text space in thereject message, with first preference given to the absolute dose messageand second preference given to the typical dose message.

These and other features, aspect and embodiments of the invention willbe described in more detail below.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating an exemplary system in accordancewith certain exemplary embodiments of the present invention.

FIG. 2 is a flow chart illustrating an exemplary look-alike sound-alikemedication error messaging method in accordance with certain exemplaryembodiments of the present invention.

FIG. 3 is a flow chart illustrating an exemplary Absolute Dose Screeningprocess in accordance with certain exemplary embodiments of the presentinvention.

FIG. 4 is a flow chart illustrating an exemplary Typical Dose Screeningprocess in accordance with certain exemplary embodiments of the presentinvention.

FIG. 5 is a flow chart illustrating an exemplary Likelihood Screeningprocess in accordance with certain exemplary embodiments of the presentinvention.

FIG. 6 is a flow chart illustrating an exemplary reject message buildand delivery method in accordance with certain exemplary embodiments ofthe present invention.

DETAILED DESCRIPTION OF THE INVENTION

The present invention provides systems and methods for look-alikesound-alike (“LASA”) medication error messaging. The systems and methodsof the present invention monitor prescription transactions and returnappropriate messages to pharmacists or other health care providers whenthe characteristics of a prescription transaction indicate thepossibility that a different medication, different dispense quantity ordifferent daily supply is more appropriate. One or more screeningprocesses are used to screen prescription transactions for possiblemedication errors. The invention provides flexibility for activating anddeactivating certain screening processes, the choice of which and thesensitivity of included parameter settings will determine how many LASAmedication error messages are returned to the pharmacist or other healthcare provider and the content of those messages.

The terms “medication” and “drug” are used synonymously herein. Theterms “medication name” and “drug name” may be used herein to refergenerally to either a brand name or a generic name of a medication. A“drug product” is the specific item dispensed to the patient and isidentified by the ingredient(s)/strength(s)/dosage form combinationdispensed to patient. Drug products are commonly identified by a uniqueidentifier, examples of which include, but are not limited to, GenericCode Number sequence numbers (“GCN*SEQNO”) and Generic ProductIdentifiers (“GPI”).

A National Drug Code number (“NDC#”), identifies the labeler/vendor,product, and trade package size. Thus, although multiple drug productsmay share a common ingredient(s)/strength(s)/dosage form combination,each drug product having a different brand, package size orlabeler/vendor is identified by a unique NDC#. In certain embodiments ofthe present invention, drug products are identified by NDC# inprescription claim transactions. In other embodiments, such as thoseinvolving electronic prescriptions or other types of prescriptiontransactions, more generic product identifiers may be used.

As mentioned previously, a “LASA drug pair” is defined herein as two ormore drug names that are known to be confused with each other. Each drugname in a LASA drug pair may be referred to as a “LASA alternative drugname” with respect to the other member(s) of the pair. Each LASAalternative drug name may be associated with one or more drug products,referred to herein as LASA alternative drug products.

In certain embodiments, a submitted NDC# is mapped to a drug product andany LASA medication error screening is performed at the drug productlevel. Performing LASA medication error screening at the drug productlevel allows all brand name and generic versions of a particular drugproduct to be taken into account. For example, a prescription may havebeen written for an originally prescribed drug product (e.g., a brandname), but the pharmacist may specify the NDC# of a substitute drugproduct (e.g., a generic alternative) in the prescription transaction.While a LASA medication error may involve the originally prescribed drugproduct or the substitute drug product, screening on the NDC# levelwould only account for potential LASA medication errors involving thesubstitute drug product.

One screening process that may be employed is referred to herein as the“Absolute Dose Screening” process. The Absolute Dose Screening processdetermines if the calculated daily dose (i.e., quantity to be dispenseddivided by days supply) of a prescription transaction exceeds thehighest dose or falls below the lowest dose allowable for the drugproduct to be dispensed. When the calculated dosage fall outside theabsolute dosing range, the drug name, dispense quantity and days supplyshould be verified. Absolute minimum dose values and absolute maximumdose values are determined from a review of manufacturers' labeling andstandard reference texts and are intended to identify the extremes ofthe dosing range for drug products. Absolute minimum dose values andabsolute maximum dose values may be stored in a database that is queriedduring the Absolute Dose Screening process.

Another screening process is referred to herein as the “Typical DoseScreening” process. Typical Dose Screening determines whether thecalculated daily dose of a prescription transaction is typical oratypical for a given patient, as defined by actual prescribing patterns.The calculated daily dose is checked against “Common Daily Dose” valuesand “Most Common Daily Dose” values. These values may be derived fromanalysis of historical prescription transactions data for given drugproducts and may be stored in a database that is queried during theTypical Dose Screening process. When a calculated atypical daily dose isdetected, a determination may be made as to whether the daily dose istypical for any LASA alternative drug products. If so, the pharmacist orother health care provider may be alerted as to the potential of alook-alike sound-alike medication error.

A further screening process is referred to herein as the “LikelihoodScreening” process. Likelihood Screening determines a relativeprobability that a prescription transaction represents a potentiallook-alike sound-alike medication error. A drug product may be assigneda “Likelihood Indicator” in relation to each LASA drug pair with whichit is associated. A Likelihood Indicator represents the likelihood ofthe drug product being incorrectly dispensed due to confusion caused bylook-alike sound-alike medications. Likelihood Indicators may be storedin a database and may be pre-determined based on several factors,including but not limited to: similarity of drug names, frequency ofdispense of drug products, similarity of strength as compared to LASAalternative drug product(s), same strength as compared to LASAalternative drug product(s), number of LASA pairs with which the drugproduct is associated, newness of the drug product to the marketplaceand/or availability as non-solid oral products.

Exemplary embodiments of the present invention will hereinafter bedescribed with reference to the figures, in which like numerals indicatelike elements throughout the several drawings. FIG. 1 is a block diagramillustrating an exemplary operating environment for implementation ofcertain embodiments of the present invention. The exemplary operatingenvironment encompasses a pharmacy point-of-service (“POS”) device 102,a host server 104 and a payer system 108, which are each configured foraccessing and reading associated computer-readable media having storedthereon data and/or computer-executable instructions for implementingthe various methods of the present invention. Generally, network devicesand systems include hardware and/or software for transmitting andreceiving data and/or computer-executable instructions over acommunications link and a memory for storing data and/orcomputer-executable instructions. Network devices and systems may alsoinclude a processor for processing data and executingcomputer-executable instructions, as well as other internal andperipheral components that are well known in the art. As used herein,the term “computer-readable medium” describes any form of memory or apropagated signal transmission medium. Propagated signals representingdata and computer-executable instructions are transferred betweennetwork devices and systems.

As shown in FIG. 1, a pharmacy POS device 102 may be in communicationwith the host server 104 via a network 106. The pharmacy POS device 102may be configured for receiving prescription claim data, creatingprescription transactions therefrom and transmitting said prescriptiontransactions to the host server 104. Prescription claim data includesany data that is typically provided by a patient, pharmacist and/orother health care provider in relation to filling a prescription and/orrequesting approval or authorization for payment from a payer or claimadjudicator. A payer may be an insurance company, a financialinstitution or another financial service provider. Prescription claimdata may be input to the pharmacy POS device 102 by a pharmacist orother health care provider or may be received by the pharmacy POS device102 in electronic form from an electronic prescription service (notshown). The pharmacy POS device 102 may be configured for handling othertypes of prescription transactions.

Prescription transactions are electronic records or messages intended tofacilitate the communication of prescription information. For example,prescription claim transactions are intended to communicate prescriptionclaim data between pharmacies and payers. Although prescription claimtransactions will be discussed hereinafter, it should be understood thatthe various systems and method of the invention may be practiced inconnection with other types of prescription transactions orindependently of prescription transactions (e.g., raw prescriptiondata). The content and format of a prescription claim may vary dependingon which standard or protocol is used. In general, however, prescriptionclaim transactions will identify at least the drug product to bedispensed (e.g., by NDC#), the quantity to be dispensed and the dayssupply, whether the prescription claim relates to a new prescription ora refill prescription and billing-related information.

Prescription claim transactions may be transmitted from the pharmacy POSdevice 102 to the host server 104 in batch, real-time or near real-time.In certain embodiments, the host server 104 may serve as a clearinghousefor multiple payer systems 108. Payer systems 108 may include systemsoperated by insurance companies, financial institutions and otherfinancial service providers. In its capacity as a clearinghouse, thehost server 104 parses prescription claim transactions and forwards themto the appropriate payer system 108 for processing. Approval,authorization or rejection messages may be returned to the host server104 from the payer systems 108 and such messages may be forwarded by thehost server 104 to the pharmacy POS device 102.

In serving as a clearinghouse, the host server 104 may also beconfigured for performing pre-processing and post-processing ofprescription claim transactions. Pre-processing and post-processingrefers to real-time or near real-time validation and management ofprescription claim data in order to maximize prescription claimreimbursement and minimize claim submission errors. Pre-processing andpost-processing may generate messaging alerts and/or retrospectivereports supporting “usual and customary” price comparisons, averagewholesale price (“AWP”) edits, dispense as written (“DAW”) brandappropriateness, and numerous other screening processes for facilitatingrapid and accurate validation of prescription claims.

In accordance with the present invention, the host server 104 may beconfigured for performing certain screening processes for the detectionof possible LASA medication errors. In the case where the host server104 functions as a clearinghouse, the screening processes for detectionof possible LASA medication errors may be implemented as pre-processingand/or post-processing methods. In other embodiments, the host server104 may not serve as a clearinghouse for prescription claim transactionsand may be dedicated to performing such tasks as LASA medication errorscreening. The LASA medication error screening processes of the presentinvention may be designed to generate alerts (also referred to as“Reject Messages”) that are transmitted to the pharmacy POS device 102when a potential LASA medication error is detected. Reject Messages mayindicate that a prescription claim has been rejected, provide apharmacist with information about the potential LASA medication errorand may encourage the pharmacist to verify the prescription claim data.The LASA medication error screening processes are also designed tocapture certain prescription claim data for subsequent analysis andreporting related to LASA medication errors.

The pharmacy POS device 102 may be any processor-driven device, such asa personal computer, laptop computer, handheld computer and the like. Inaddition to a processor 110, the pharmacy POS device 102 may furtherinclude a memory 112, input/output (“I/O”) interface(s) 114 and anetwork interface 116. The memory 112 may store data files 118 andvarious program modules, such as an operating system (“OS”) 120 and apractice management module 122. The practice management module 122 maycomprise computer-executable instructions for performing variousroutines, such as those for creating and submitting prescription claimtransactions. I/O interface(s) 114 facilitate communication between theprocessor 110 and various I/O devices, such as a keyboard, mouse,printer, microphone, speaker, monitor, etc. The network interface 116may take any of a number of forms, such as a network interface card, amodem, etc. These and other components of the pharmacy POS device 102will be apparent to those of ordinary skill in the art and are thereforenot discussed in more detail herein.

Similarly, the host server 104 may be any processor-driven device thatis configured for receiving and fulfilling requests related toprescription claim transactions. The host server 104 may thereforeinclude a processor 126, a memory 128, input/output (“I/O”) interface(s)130 and a network interface 132. The memory 128 may store data files 134and various program modules, such as an operating system (“OS”) 136, adatabase management system (“DBMS”) 138 and a LASA error messagingmodule 140. The LASA error messaging module 140 may comprisecomputer-executable instructions for performing various screeningprocesses for detecting possible LASA medication errors and for managingrelated messaging and reporting functions. The host server 104 mayinclude additional program modules (not shown) for performing otherpre-processing or post-processing methods and for providingclearinghouse services. Those skilled in the art will appreciate thatthe host server 104 may include alternate and/or additional components,hardware or software. In addition, the host server 104 may be connectedto a local or wide area network (not shown) that includes other devices,such as routers, firewalls, gateways, etc.

The host server 104 may include or be in communication with one or moredatabase 105. The database 105 may store, for example, data relating toLASA drug pairs, Most Common Daily Dose values, Common Daily Dosevalues, Likelihood Indicators and other data used in the various LASAmedication error screening processes of the present invention. Thedatabase 105 may also store reports and other data relating to theresults of the LASA medication error screening processes. The database105 may of course also store any other data used or generated by thehost server 104, such as data used in other pre-processing andpost-processing methods and reports generated thereby. Although a singledatabase 105 is referred to herein for simplicity, those skilled in theart will appreciate that multiple physical and/or logical databases maybe used to store the above mentioned data. For security, the host server104 may have a dedicated connection to the database 105, as shown.However, the host server 104 may also communicate with the database 105via a network 106.

The network 106 may comprise any telecommunication and/or data network,whether public or private, such as a local area network, a wide areanetwork, an intranet, an internet and/or any combination thereof and maybe wired and/or wireless. Due to network connectivity, variousmethodologies as described herein may be practiced in the context ofdistributed computing environments. Although the exemplary pharmacy POSdevice 102 is shown for simplicity as being in communication with thehost server 104 via one intervening network 106, it is to be understoodthat any other network configuration is possible. For example, thepharmacy POS device 102 may be connected to a pharmacy's local or widearea network, which may include other devices, such as gateways androuters, for interfacing with another public or private network 106.Instead of or in addition to a network 106, dedicated communicationlinks may be used to connect the various devices of the presentinvention.

Those skilled in the art will appreciate that the operating environmentshown in and described with respect to FIG. 1 is provided by way ofexample only. Numerous other operating environments, systemarchitectures and device configurations are possible. For example, theinvention may in certain embodiments be implemented in a non-networkedenvironment, in which a stand-alone pharmacy POS device 102 executes oneor more LASA error messaging module(s) 140. Accordingly, the presentinvention should not be construed as being limited to any particularoperating environment, system architecture or device configuration.

FIG. 2 is a flow diagram illustrating an exemplary process for screeningprescription claims for potential LASA medication errors 200 inaccordance with certain embodiments of the invention. The method beginsat starting block 201 and progresses to step 202, where a prescriptionclaim transaction is received. Next at step 204, the transaction isparsed to identify the submitted drug product, daily dosage and whetherthe transaction relates to a new prescription or a refill. The drugproduct and daily dosage values may be specified in the prescriptionclaim transaction or may need to be derived from the prescription claimdata. For example, the prescription claim data included in thetransaction may include an NDC# or other code to identify the submitteddrug product. In certain embodiments where LASA medication errorscreening is performed on the drug product level, a submitted NDC# isidentified from the prescription claim data and a database 105 isqueried to map the submitted NDC# to a drug product. The prescriptionclaim data may also identify a quantity to be dispensed and a dayssupply, from which a submitted daily dosage value can be derived.

At step 206, a determination is made as to whether the submitted drugproduct is a member of an active LASA drug pair. The determination ofstep 206 may be made, for example, by interrogating a database 105 basedon the submitted drug product. The database 105 may include a tablepopulated with any or all available LASA drug pairs, each of which maybe mapped to one or more drug products. LASA drug pairs may be definedor identified by an industry standards organization, such as USP, ISMPor FDA. LASA drug pairs may also be defined or identified bypharmaceutical companies, pharmacy managers, health care providers, etc.

In certain embodiments, each entry in a LASA drug pair database tablemay indicate whether the LASA drug pair is active or inactive. ActiveLASA drug pairs may be searched, while inactive LASA drug pairs may beignored. A pharmacy manager or other system administrator may beprovided with the ability to define whether a LASA drug pair is to beactive or inactive and may thus be able to control which LASA drug pairsare to be included in the LASA medication error screening processes.Other embodiments may not distinguish between active and inactive LASAdrug pairs, meaning that all LASA drug pairs in the database table areactive and will be searched.

If the submitted drug product is not a member of an active LASA drugpair, the method proceeds to step 208 to await the next prescriptionclaim transaction, the receipt of which will cause the method to berepeated, as described above, from step 202. However, if the submitteddrug product is a member of an active LASA drug pair, the methodadvances to step 210 for a determination of whether the Absolute DoseScreening process is activated. As mentioned previously and described ingreater detail below, Absolute Dose Screening may be used to determinewhether the submitted daily dosage falls within a range defined by anabsolute maximum dosage and an absolute minimum dosage for the submitteddrug product. The Absolute Dose Screening process may be deactivated bythe pharmacy manager or other system administrator.

If Absolute Dose Screening is activated, the method moves to step 212,where Absolute Dose Screening is performed in order to determine whetherthe submitted daily dosage meets the absolute dosing criteria for thesubmitted drug product. The Absolute Dose Screening process may generatean Absolute Dose Message to indicate whether the submitted daily dosagemeets the absolute dosing criteria for the submitted drug product.Depending on a configurable “Edit Action” parameter of the Absolute DoseScreening process, any Absolute Dose Message may or may not be deliveredto the pharmacist as part of a “Reject Message.” Reject Messages may beused to indicate to the pharmacist that the prescription claim has beenrejected for a particular reason, which may include non-compliance withabsolute dosing criteria. The Absolute Dose Screening process may alsospecify that the Absolute Dose Message and/or certain prescription claimdata should be captured for subsequent analysis and reporting.

In accordance with certain embodiments, Edit Action parameters may beused to define the situations in which a prescription claim should berejected, the situations in which prescription claims transactionsshould be recorded for later analysis and reporting and the situationsin which no action should be taken. In most cases, all rejected claimswill likely be recorded. However, some claims may be recorded even ifthey are not rejected. For example, a prescription claim may violateabsolute dosing criteria but for some reason (e.g., claim relates to arefill prescription) the claim may not be rejected. Such a claim maystill be recoded for later analysis and reporting. In accordance withcertain embodiments, the Edit Action parameters may be configured by thepharmacy manager or other system administrator.

After performance of Absolute Dose Screening at step 212, or if AbsoluteDose Screening was determined to be inactive at step 210, the methodadvances to step 214 for a determination as to whether Typical DoseScreening is activated. As mentioned previously and described in greaterdetail below, Typical Dose Screening may be used to determine whetherthe submitted daily dosage is equivalent to statistically-determinedMost Common Daily Dosage (“MCDD”) or Common Daily Dosage (“CDD”) valuesfor the submitted drug product. If the submitted dosage is notequivalent to the MCDD for the submitted drug product, determinationsmay be made as to whether it is equivalent to the MCDD or CDD values forany LASA alternative drug product. If the submitted dosage is notequivalent to the MCDD for the submitted drug product, but is equivalentto the MCDD or CDD values for a LASA alternative drug product, apossible LASA medication error may exist. If the submitted dosage is notequivalent to the MCDD or CDD values for either the submitted drugproduct or any LASA alternative drug product, a dosing error may existindependent of a LASA medication error. The Typical Dose Screeningprocess may be deactivated by the pharmacy manager or other systemadministrator.

If Typical Dose Screening is activated, the method moves to step 216,where Typical Dose Screening is performed in order to determine whetherthe submitted daily dosage meets the typical dosing criteria for thesubmitted drug product. The Typical Dose Screening process may generatea Typical Dose Message to indicate whether the submitted daily dosagemeets the typical dosing criteria for the submitted drug product.Depending on a configurable “Edit Action” parameter of the Typical DoseScreening process, any Typical Dose Message may or may not be deliveredto the pharmacist as part of a Reject Message. The Typical Dose EditAction may also specify that the Typical Dose Message and/or certainprescription claim data should be captured for subsequent analysis andreporting.

After performance of Typical Dose Screening at step 216, or if TypicalDose Screening was determined to be inactive at step 214, the methodadvances to step 218 for a determination as to whether LikelihoodScreening is activated. As mentioned previously and described in greaterdetail below, Likelihood Screening may be used to determine a relativeprobability that a prescription claim includes a potential LASAmedication error. The Likelihood Screening process may be deactivated bythe pharmacy manager or other system administrator.

If Likelihood Screening is activated, the method moves to step 220,where Likelihood Screening is performed in order to determine aLikelihood Indicator for the submitted drug product in relation to eachLASA drug pair with which the submitted drug product is associated. TheLikelihood Screening process may generate a Likelihood Message based ona Likelihood Indicator. Depending on a configurable “Edit Action”parameter of the Likelihood Screening process, any Likelihood Messagemay or may not be delivered to the pharmacist as part of a RejectMessage. The Likelihood Edit Action may also specify that the LikelihoodMessage and/or certain prescription claim data should be captured forsubsequent analysis and reporting.

After performance of Likelihood Screening at step 220, or if LikelihoodScreening was determined to be inactive at step 218, the method advancesto step 222 where a Reject Message is built, if appropriate. Inexemplary embodiments, a Reject Message may be built when an Edit Actionparameter from at least one of the screening processes indicates thatthe prescription claim should be rejected. If the Absolute Dose EditAction indicates that the prescription claim should be rejected, theAbsolute Dose Message may be inserted into the Reject Message. If theTypical Dose Edit Action indicates that the prescription claim should berejected, the Typical Dose Message may be inserted into the RejectMessage. If the Likelihood Edit Action indicates that the prescriptionclaim should be rejected, the Likelihood Message may be inserted intothe Reject Message.

However, inclusion of multiple messages in a Reject Message may beredundant or otherwise unnecessary. Therefore, if the prescription claimtransaction is to be rejected based on the results of multiple screeningprocesses, logic may be employed to prioritize and select the message ormessages to be included in the Reject Message. After a Reject Message isbuilt, it is delivered to the pharmacist. In exemplary embodiments,Reject Messages are delivered to the pharmacist in the form ofelectronic messages to the practice management module 122 executed bythe pharmacy POS device 102. Such electronic messages may be deliveredvia the network 106 as propagated signals.

Next, the method proceeds to step 224, where selected messages and/orprescription claim data is recorded, if appropriate, for subsequentreporting and analysis. Again, whether or not recording of prescriptionclaim data is appropriate may be conditioned on the Edit Actionparameters that were returned by each screening process. If at least oneEdit Action parameter indicates that the prescription claim should berejected, prescription claim data and/or appropriate message(s) shouldbe recorded. Also, if at least one Edit Action parameter indicates thatthe prescription claim should be captured (i.e., recorded but notrejected), such action should be taken. If all Edit Action parametersindicate that no action should be taken for the prescription claim, thenno prescription claim data or messages are recorded. Edit Actionparameters may also dictate which prescription claim data is to berecorded. For example, different data may need to be recorded forreporting and analysis of non-compliance with typical dosing criteriathan may need to be recorded for reporting and analysis ofnon-compliance with absolute dosing criteria.

Following step 224, the method ends at step 226. Those skilled in theart will appreciate that other screening processes, in addition toAbsolute Dose Screening, Typical Dose Screening and Likelihood Screeningmay be incorporated into the overall method 200 of the presentinvention. The above-mentioned analysis and reporting of recordedprescription claim data may be performed on all recorded data in orderto form generalized conclusions regarding LASA medication errors.Alternatively, the recorded data may be analyzed at the pharmacy chainlevel, pharmacy store level, physician level, patient demographicgrouping level, etc. in order to form more specific conclusionsregarding LASA medication errors.

Although not illustrated in FIG. 2, it should be appreciated that thesystems and methods of the present invention may be configured to accept“overrides” from pharmacists or system administrator. In other words, apharmacist or system administrator may be able to override a rejectionof a prescription claim and cause the prescription claim to beprocessed. The pharmacists or system administrator may need to provide acode or some other identifier that indicates his/her authority torequest the override. The pharmacist may need to change some portion ofthe prescription claim data in order to request an override. In certainembodiments, if an override is submitted, any messages previouslyproduced by the LASA medication error screening processes may beattached to post-edit message delivered to the pharmacist.

Edit overrides and transactions resulting therefrom may also be recordedfor subsequent reporting and analysis. Comparison of all versions of aparticular prescription claim through a process known as “PrescriptionMatching” can provide useful insight into the reason(s) why thepharmacist may have made an error or why the reject message was a falsepositive. Prescription Matching involves identifying all prescriptionsclaims having the same date of service and prescription number from thesame pharmacy. The latest such prescription claim is designated as the“Matching Prescription” and is given a key to link it back to priorversion(s) of the transaction that invoked the reject message.

The systems and methods of the present invention may be configured withan “Always Message” option for certain types of prescription claimtransactions. In an Always Message mode, the LASA medication errorscreening processes may be skipped and an administratively-definedmessage may be sent to the pharmacist. For example, the Always Messagemode may be configured to send a customized warning message to apharmacist every time a particular drug product is identified in aprescription claim transaction. Prescription claim data may be capturedfor reporting and analysis in an Always Message situation.

FIG. 3 is a flow chart illustrating an exemplary Absolute Dose screeningprocess 212 in accordance with one or more embodiments of the presentinvention. The Absolute Dose screening process 212 begins at startingblock 301 and then proceeds to step 302, where the absolute minimumdoses per day for the submitted drug product is determined. The absoluteminimum doses per day may be determined by interrogating a databasestoring such information. Absolute minimum daily dosages are defined byvarious text references known in the health care industry, such as thePhysicians Desk Reference (“PDR”), the United States Pharmacopedia DrugInformation (“USPDI”) and the like, as well as by the United States Foodand Drug Administration (“USFDA”). Next at step 304, the absolutemaximum doses per day for the submitted drug product is determined.Again, information regarding maximum daily dosages for drug products maybe stored in and retrieved from a database.

At step 306 a determination is made as to whether the submitted dosesper day is less than the absolute minimum doses per day for thesubmitted drug product. If so, the method proceeds to step 308 for adetermination of whether the prescription claim relates to a newprescription. If the prescription does not relate to a new prescription,the method moves to step 310 to determine the Absolute Dose Message andthe administratively-defined Absolute Dose Edit Action for refills withlower than the absolute minimum daily dosage. If the prescription doesrelate to a new prescription, the method moves to step 312 to determinethe Absolute Dose Message and the administratively-defined Absolute DoseEdit Action for new prescriptions with lower than the absolute minimumdaily dosage. After determining an Absolute Dose Message and an AbsoluteDose Edit Action at either step 310 or step 312, the method ends at step324.

As mentioned previously, Absolute Dose Messages may be used to indicatewhether the submitted daily dosage meets the absolute dosing criteriafor the submitted drug product. Absolute Dose Messages may take anyappropriate form and may be used, for example, to inform or remind thepharmacist of the absolute maximum or minimum dosages for the submitteddrug product. In accordance with certain embodiments, Edit Actionoptions may be: “Reject,” “Capture” and “None.” The Reject Edit Actionmay be used to indicate that the Absolute Dose Message should beincluded in a Reject Message sent to the pharmacist. The Capture EditAction may be used to indicate that the Absolute Dose Message and otherprescription claim transaction data should be recorded for lateranalysis, but not included in a Reject Message. The None Edit Action maybe used to indicate that no recording or Reject Message is required.Other Edit Actions are possible. For example, an Edit Action may bedefined to indicate that the Absolute Dose Message should be sent to thepharmacist as an information message when the prescription claim is notrejected. Or, an Edit Action may be defined to indicate that theAbsolute Dose Message should be printed on a warning label. These andother examples of Edit Actions are contemplated in connection with allscreening processes of the present invention.

Edit Actions are referred to as being administratively-defined because asystem administrator, such as a pharmacy manager, may determine whichEdit Action is applicable to a given situation. As an example, forvarious reasons one system administrator may determine that a RejectEdit Action is appropriate when a prescription claim transaction relatesto a refill with lower than the absolute minimum daily dosage. Anothersystem administrator may determine that a Capture Edit Action isappropriate for the same situation. Edit Actions for given situationsmay be re-set at any time. For example, if it is determined that aReject Edit Action for a particular situation yields too may “falsepositive” LASA medication errors, the Edit Action for that situation maybe changed to Capture.

Absolute Dose Messages and Absolute Dose Edit Actions may be stored inone or more look-up tables or other suitable data structures within adatabase 105 accessible by the host server 104. Table 1 below is anexample of such a look-up table. Table 1 is provided by way ofillustration only. Other Absolute Dose Messages and/or Absolute DoseEdit Actions may be used in situations where a submitted daily dosageexceeds an absolute maximum daily dosage or is less than an absoluteminimum daily dosage.

TABLE 1 Absolute Dose Messages and Edit Actions Submitted Daily AbsoluteDosage New Rx Refill Rx Dose Message: <Absolute Min Edit Action = EditAction = “[XXX] Doses/Day Reject None Minimum” >Absolute Max Edit Action= Edit Action = “[XXX] Doses/Day Reject None Maximum”

Returning to step 306, if it is determined that the submitted doses perday is not less than the absolute minimum doses per day for thesubmitted drug product, the method proceeds to step 314 for adetermination of whether the submitted doses per day is greater than theabsolute maximum doses per day for the submitted drug product. If thesubmitted doses per day is greater than the absolute maximum doses perday for the submitted drug product, the method proceeds to step 316,where a determination is made as to whether the prescription claimrelates to a new prescription. If the prescription does not relate to anew prescription, the method moves to step 318 to determine the AbsoluteDose Message and the administratively-defined Absolute Dose Edit Actionfor refills exceeding the absolute maximum daily dosage. If theprescription does relate to a new prescription, the method moves to step320 to determine the Absolute Dose Message and theadministratively-defined Absolute Dose Edit Action for new prescriptionsexceeding the absolute maximum daily dosage. Again, Absolute DoseMessages and Absolute Dose Edit Actions may be determined by consultinga look-up table, such as the above-illustrated Table 1, or other datastructure containing such information. After determining an AbsoluteDose Message and an Absolute Dose Edit Action at either step 318 or step320, the method ends at step 324.

If at step 314 it is determined that the submitted doses per day is notgreater than the absolute maximum doses per day for the submitted drugproduct, the submitted doses per day satisfies the absolute dosingcriteria for the submitted drug product. In that case, the method movesto step 322 where the Absolute Dose Edit Action is set to None.Following step 322, the method ends at step 324.

FIG. 4 is a flow chart illustrating an exemplary Typical Dose screeningprocess 216 (from FIG. 2) in accordance with one or more embodiments ofthe present invention. The Typical Dose screening process 216 begins atstarting block 401 and then proceeds to step 402, where the all activeLASA drug pairs associated with the submitted drug product are selected.As mentioned previously, a list or table of LASA drug pairs may bestored in a database 105. A system administrator, such as a pharmacymanager, may specify which of the LASA drug pairs is to be considered“active” and therefore examined during the Typical Dose Screeningprocess 216. It is assumed, based on the flow of FIG. 2, that thesubmitted drug product is associated with at least one active LASA drugpair; if not, the Typical Dose Screening process 216 would not beperformed. However, those skilled in the art will appreciate that theTypical Dose Screening process 216 may be used outside the context ofFIG. 2 and could thus be modified to include a database check for atleast one active LASA drug pair associated with the submitted drugproduct.

After selecting all active LASA drug pair associated with the submitteddrug product, the method advances to step 404, where the clinicalsignificance for each selected LASA drug pair is determined. Clinicalsignificance values may be stored in a database 105 in association withcorresponding LASA drug pairs and may be modified as appropriate.Clinical significance may be represented by a clinically-determinedvalue assigned to a LASA drug pair. Clinical significance may be used toquantify the consequences of a LASA medication error caused bysubstituting one drug product associated with the LASA drug pair with aLASA alternate drug product associated with the LASA drug pair. Forexample, a clinical significance of 1 may be used to indicate that aLASA medication error could be harmful or fatal; a clinical significanceof 2 may be used to indicate that a LASA medication error could have amild effect on the patient; and a clinical significance of 3 may be usedto indicate that a LASA medication error could little or no effect onthe patient. Clinical significance values may be determined in numerousmanners and may be derived from clinical data, knowledge and/orexpertise.

After determining the clinical significance values, the method proceedsto step 406 to determine the Most Common Daily Dosage (“MCDD”) valuesfor the submitted drug product and for any LASA alternative drugproducts associated with the selected LASA drug pairs. Next at step 408,Common Daily Dosage (“CDD”) values are determined for the submitted drugproduct and for any LASA alternative drug products associated with theselected LASA drug pairs. MCDD values and CDD values may be determinedat step 406 and 408 by consulting a look-up table or other suitable datastructure (e.g., stored in database 105) containing such information.Those skilled in the art will appreciate that steps 402, 404, 406 and408 may be performed in a different order, simultaneously, etc.

MCDD and CDD values may be derived in a variety of ways, such as throughstatistical analysis of historical prescription claim data. Thoseskilled in the art will appreciate that suitable statistical analysismethods include, but are not limited to, cluster analysis, logisticregression, Chi-square tests and Graphing methods. Historicalprescription claims data may be analyzed using one or more of suchmethods, or other suitable methods, in order to identify actualprescribing patterns for drug products, from which statistically validCDD and MCDD values can be derived. Depending on the criteria used todefine CDD and MCDD values, a given drug product may have more than oneCDD. Likewise, a given drug product may not have a CDD or an MCDD.

By way of illustration only and not by way of limitation, CDD and MCDDvalues may be derived as follows. A sample of not less than apredetermined number (e.g., 100) of prescription claim transactionsinvolving a given drug product may be analyzed to identify historicalprescribing patterns for the drug product. If a particular daily dose+/−a deviation (e.g., 0.15 units) is determined to have been prescribedin a predetermined percentage (e.g., 10% or more) of all transactions inthe sample, then that daily dose constitutes a CDD. If a first CDD is adaily dosage that was prescribed in a second predetermined percentage(e.g., 50% or more) of all transactions in the sample and a second CDDis significantly different from the first CDD (e.g., by Chi Square testfor equal proportions in two runs of 25 randomly selected transactions),then the first CDD is the MCDD for the drug product. If a first CDD is adaily dosage that was prescribed in a third predetermined percentage(e.g., greater than 50%) of the transactions in the sample and a secondCDD is found that is not significantly different from the first CDD(e.g., by Chi Square test for equal proportions in two runs of 25randomly selected transactions), then no MCDD exists.

In certain embodiments, CDD and/or MCDD values for a drug product may betied to one or more patient demographic groups, such as those based ongender or age. In other words, the CDD and/or MCDD values for a givendrug product may be different for different types of patient demographicgroups. As an example, a drug product may have a MCDD value for womenthat is different from its MCDD value for men. Patient demographic groupcharacteristics may thus be built into the statistical analysis model(s)used to derive CDD and MCDD values from historical prescription claimsdata.

At step 410 a determination is made as to whether the submitted dailydosage is equal to the MCDD for the submitted drug product. If thesubmitted daily dosage is equal to the MCDD for the submitted drugproduct, the method moves to step 412, where the Typical Dose EditAction for the transaction is set to “None.” Following step 412, themethod ends at step 438. However, if it is determined at step 410 thatthe submitted daily dosage is not equal to the MCDD for the submitteddrug product, the method proceeds to step 414, where a determination ismade as to whether the submitted daily dosage is equal to the CDD forthe submitted drug product.

If the submitted daily dosage is equal to the CDD for the submitted drugproduct, the method advances to step 416, where it is determined whetherthe submitted daily dosage is equal to the MCDD for any LASA alternativedrug product associated with the selected LASA drug pairs. If thesubmitted daily dosage is not equal to the MCDD for any LASA alternativedrug product associated with the selected LASA drug pairs, the methodmoves to step 412, where the Typical Dose Edit Action for theprescription claim is set to “None.” Following step 412, the method endsat step 438. If, however, the submitted daily dosage is determined atstep 416 to be equal to the MCDD for any LASA alternative drug product,a potential LASA medication error is deemed to have been identified andthe method proceeds to step 418.

The particular Typical Dose Message and Typical Dose Edit Action to beapplied may depend on whether the submitted prescription claim relatesto a new prescription or to a refill prescription. Also, the TypicalDose Message and Typical Dose Edit Action to be applied may depend onthe clinical significance assigned to the involved LASA drug pair. Thus,at step 418, a check is made to determine whether the prescription claimrelates to a new prescription. If the prescription claim relates to anew prescription, the method proceeds to step 420, where the TypicalDose Message and Typical Dose Edit Action are determined for a newprescription with a possible LASA medication error, based on clinicalsignificance. If the prescription claim does not relate to a newprescription, the method proceeds to step 422 where the Typical DoseMessage and Typical Dose Edit Action are determined for a refillprescription with a possible LASA medication error, based on clinicalsignificance.

To determine applicable Typical Dose Messages and Typical Dose EditActions, a look-up table or other data structure containing suchinformation may be consulted. An exemplary look-up table is illustratedbelow as Table 2.

TABLE 2 Typical Dose Messages and Edit Actions Submitted Alternate DrugDrug Clinical New Refill Product Product Significance Rx Rx Typical DoseMessage MCDD — — Edit Action = Edit Action = — None None CDD MCDD 1 EditAction = Edit Action = “Possible LASA w/ Reject None [LASA AlternativeDrug Name(s)]” CDD MCDD 2 Edit Action = Edit Action = “Possible LASA w/Capture None [LASA Alternative Drug Name(s)]” CDD MCDD 3 Edit Action =Edit Action = “Possible LASA w/ Capture None [LASA Alternative DrugName(s)]” Atypical CDD 1 Edit Action = Edit Action = “AtypicalDoses/Day- Reject None Check for LASA w/ [LASA Alternative DrugName(s)]” Atypical CDD 2 Edit Action = Edit Action = “AtypicalDoses/Day- Reject None Check for LASA w/ [LASA Alternative DrugName(s)]” Atypical CDD 3 Edit Action = Edit Action = “AtypicalDoses/Day- Reject None Check for LASA w/ [LASA Alternative DrugName(s)]” Atypical Atypical 1 Edit Action = Edit Action = “AtypicalDoses/Day- None None Check Dosing” Atypical Atypical 2 Edit Action =Edit Action = “Atypical Doses/Day- None None Check Dosing” AtypicalAtypical 3 Edit Action = Edit Action “Atypical Doses/Day- None NoneCheck Dosing”

Table 2 above, is shown by way of example only. Other Typical DoseMessages and Typical Dose Edit Actions may be associated with clinicalsignificance values and CDD/MCDD/Atypical situations. Table 2illustrates one fictitious system administrator's desire to reject aprescription claim when the submitted daily dosage is equal to the CDDfor the submitted drug product and the clinical significance value is “1” or when the submitted daily dosage is atypical for the submitted drugproduct, but is equal to the CDD for a LASA alternative drug productassociated with the selected LASA drug pairs. In the case where thesubmitted daily dosage is equal to the MCDD for the submitted drugproduct, a Typical Dose Message so indicating may be used, or no TypicalDose Message may be used. Multiple LASA alternative drug names may beinserted into the Typical Dose Message and may be ordered according toclinical significance. In certain embodiment, all LASA alternative drugnames are inserted if the submitted daily dosage is equal to the MCDD orCDD for any one LASA alternative drug product associated with theselected LASA drug pairs.

Returning to step 414 if the submitted daily dosage is determined to notbe equal to the CDD for the submitted drug product, the method proceedsto step 424, where it is determined whether the submitted daily dosageis equal to the CDD for any LASA alternative drug product associatedwith the selected LASA drug pairs. If the submitted daily dosage isequal to the CDD for any LASA alternative drug product, a potential LASAmedication error is deemed to have been identified and the methodproceeds to step 426. At step 426, a check is made to determine whetherthe prescription claim relates to a new prescription. If theprescription claim relates to a new prescription, the method proceeds tostep 428 where the Typical Dose Message and Typical Dose Edit Action aredetermined for a new prescription with atypical dosing and a possibleLASA error, based on clinical significance.

If the prescription claim does not relate to a new prescription, themethod proceeds to step 430 where the Typical Dose Message and TypicalDose Edit Action are determined for a refill prescription with atypicaldosing and a possible LASA error, based on clinical significance. Again,Typical Dose Messages and Typical Dose Edit Actions may be determined byquerying a look-up table, such as Table 2, or other data structurecontaining such information. After determining a Typical Dose Messageand Typical Dose Edit Action at either step 428 or step 430, the methodends at step 438.

However, if it is determined at step 424 that the submitted daily dosageis not equal to the CDD for any LASA alternative drug product, nopotential LASA medication error is deemed to have been identified. Inthat case, the method proceeds to step 432, where a check is made as towhether the prescription claim relates to a new prescription. If theprescription claim relates to a new prescription, the method proceeds tostep 434 where the Typical Dose Message and Typical Dose Edit Action aredetermined for a new prescription with atypical dosing, based onclinical significance. If the prescription claim does not relate to anew prescription, the method proceeds to step 436 where the Typical DoseMessage and Typical Dose Edit Action are determined for a refillprescription with atypical dosing, based on clinical significance.Typical Dose Messages and Typical Dose Edit Actions may be determined,for example, from a look-up table, such as Table 2, or another datastructure containing such information. After determining a Typical DoseMessage and Typical Dose Edit Action at either step 434 or step 436, themethod ends at step 438.

In other embodiments, the Typical Dose Screening process may be adaptedfor use outside of the context of LASA medication error screening. Forexample, a prescription claim transaction may be parsed to identify asubmitted drug product and a submitted daily dosage. It may then bedetermined whether the submitted daily dosage meets statisticallyderived typical dosing criteria (e.g., a CDD) for the submitted drugproduct. If the submitted daily dosage does not meet the statisticallyderived typical dosing criteria for the submitted drug product a TypicalDose Message may be determined. In such an embodiment, a Typical DoseEdit Action may be determined based on whether the prescription claimrelates to a new prescription or a refill.

FIG. 5 is a flow chart illustrating an exemplary Likelihood Screeningprocess 220 (from FIG. 2) in accordance with certain embodiments of thepresent invention. The exemplary Likelihood Screening process seeks todetermine one or more Likelihood Indicators for the submitted drugproduct, which are used to determine a Likelihood Message. A LikelihoodIndicator is meant herein to represent a relative probability of whethera submitted drug product is involved in a LASA medication errorinvolving an associated LASA drug pair. Likelihood Indicators may bepre-determined and stored in a database in association with drugproducts, meaning that they are not dynamically determined each time theexemplary Likelihood Screening process 220 is executed. Thus, duringexecution of the Likelihood Screening process, a database may be queriedto retrieve the Likelihood Indicator(s) for a given drug product.

A variety of factors may contribute to whether a LASA medication erroris likely. Research has indicated that three significant contributors tothe likelihood of a LASA medication error involving a pair of drugs are:(1) the degree to which the drug names look or sound alike; (2) whetherone drug is available in a same-strength, a look-alike strength or asound-alike strength as the other drug; and (3) differing degrees offamiliarity that a pharmacist may have with the drugs, resulting inconfirmation bias. Other factors may enhance the likelihood of a LASAmedication error involving a pair of drug, including but not limited to,whether both drugs are available in the same dosage form and whetherboth drugs are available in non-oral solid dosage form.

Still other factors may diminish the likelihood of a LASA medicationerror. These diminishing factors include, but are not limited to, verydifferent dispensing requirements between the drugs; any action taken bythe drug manufacturer to decrease likelihood of error (e.g., change inpackaging); a low number of different LASA drug pairs in which a drugname is included; a low market share for a drug having a branded genericname; a generic drug name of a single-source brand, which is veryunlikely to be prescribed by generic drug name except in hospitalsetting.

Levenshtein Distance (LD) may be used to predict whether a pair of drugnames are sufficiently similar to give rise to a LASA medication error.Levenshtein Distance is a measure of the similarity between two strings,which are referred to herein as the “source string” and the “targetstring.” The LD is defined as the number of deletions, insertions, orsubstitutions required to transform the source string into the targetstring. For example, if the source string is “test” and the targetstring is also “test,” then LD(source, target)=0 because notransformations are needed to conform the strings. If the source stringis “test” and the target string is “tent”, then LD(source, target)=1,because one substitution (changing the letter “s” to the letter “n”) issufficient to transform the source string into the target string. Thegreater the Levenshtein distance, the more different the strings are.

In accordance with certain embodiments, a Threshold LD may be defined asthe LD between two drug names below which a LASA medication error islikely. For example, the Threshold LD may be set to 5 or anotheradministratively defined value. In the case where the Threshold LD is 5,the drug names Serzone and sertraline, which have an LD of 7, would notbe considered similar enough to give rise to a LASA medication error,based on LD alone. For two-part words separated by a space or hyphen, LDmay be calculated with and without the divider and the greater of the LDmay be ignored. For parts of drug names that could be noted differentways (e.g., “24-hour” v. “24 hr” v. “24 h”) the shortest notation may beused in calculating LD, along with the two-part rule, if necessary.Other tests for determining similarity in sound and/or appearancebetween words are known in the art and may be substituted and/or used inconjunction with the Levenshtein Distance test.

In certain embodiments, the prescribing frequency will be determined atthe generic level, but exceptions may be made for cases where theprescribing frequency of an individual drug product is not reflective ofthe frequency for the generic level. Prescribing frequencies may becategorized as either “High,” “Medium” or “Low.” The dividing linesbetween High, Medium and Low prescribing frequencies may be different indifferent embodiments. As one example, a High prescribing frequency mayassigned to drug products that account for the top 50% of the totalprescriptions for the set of all drug products associated with the LASAdrug pair list. More particularly, the total prescription count for allsuch drug products may be determined. Then all drug products may beranked in descending order by prescription count and a cumulativepercent and cumulative total may be determined for each drug product.All drug products with a cumulative percentage of 50% or greater areassigned a High prescribing frequency, while all drug products with acumulative percentage of 0.25% or less is assigned a Low prescribingfrequency. Each drug product falling in between the High and Lowcategories is assigned a Medium prescribing frequency. The number oftotal prescriptions may be based on prescriptions per pharmacist, perpharmacy or per multiple pharmacies.

In certain embodiments, a drug name of a LASA drug pair may be assigneda High prescribing frequency if any associated drug product has a Highprescribing frequency. Similarly, a drug name of a LASA drug pair may beassigned a Medium prescribing frequency if no associated drug producthas a High prescribing frequency but at least one associated drugproduct has a Medium prescribing frequency. A drug name of a LASA drugpair may be assigned a Low prescribing frequency if no associated drugproduct has a High or a Medium prescribing frequency. It may be assumedthat a LASA drug pair having a High-Low combination of prescribingfrequencies has the potential for confirmation bias. In other words, apharmacist may mistakenly attempt to dispense a first drug having a Highprescribing frequency instead of a second drug having a Low prescribingfrequency simply because he or she is more accustomed to dealing withthe first drug. It may also be assumed that a LASA drug pair having aLow-Low combination of prescribing frequencies has the potential forconfirmation bias because any individual pharmacist may have heard ofone such drug but not the other. Different assumptions regardingconfirmation bias may be drawn in other embodiments.

As used herein, the term “same-strength” refers to strength indicators(e.g., 5 mg) that are identical. Look-alike strengths are considered tobe any strength indicators with the same series of numbers, regardlessof leading or following zeros or decimal points. For example, 0.5 mg, 5mg, 50 mg and 500 mg are considered to be look-alike strengths. Sincedecimal points can sometimes be mistaken for ones, strength indicatorssuch as 0.5 and 15, for example, could also be considered look-alikestrengths in certain embodiments. Sound-alike strengths are consideredto be any strength indicators that sound similar when spoken. An exampleof sound-alike strengths are 15 mg and 50 mg.

Exemplary criteria for determining Likelihood Indicators in accordancewith certain embodiments of the invention are summarized in Table 3below. It is to be understood, however, that Table 3 is provided by wayof illustration only. Other criteria or combinations of criteria may beused to determine the likelihood of a LASA medication error. Inaddition, different weights (levels of perceived significance) may beassigned to the described and other criteria.

TABLE 3 Likelihood Indicator Look-Up Table Likelihood Indicator Criteria1 A AND B AND C OR Any Two Of A, B Or C If Both LASA Drug Pair MembersHave Same Dosage Form That Is NOT An Oral Solid AND No DecreasingFactors Are Present. 2 (A AND B) OR (B AND C) OR (A AND C) OR Any One OfA, B, Or C if Both LASA Drug Pair Members Have Same Dosage Form That IsNOT An Oral Solid AND No Decreasing Factors Are Present 3 A OR B OR C ORNone Of A, B Or C Is True AND Both LASA Drug Pair Members Have SameDosage Form That Is NOT An Oral Solid AND No Decreasing Factors ArePresent 4 None Of A Or B Or C Is True AND Both LASA Drug Pair MembersAre Oral Solids 5 None Of A Or B Or C Is True AND One Or More DecreasingFactors Are Present A = Levenshtein Distance Of 5 Or Less B = High-Lowor Low-Low Prescribing Frequencies C = Presence Of One Or More Same,Look-Alike Or Sound-Alike Strengths

Table 3 illustrates that the combination of a Levenshtein Distance of 5or less, a pair of high-low or low-low prescribing frequencies, and thepresence of at least one same, look-alike or sound-alike strengths maybe determined to give rise to the highest likelihood of a LASAmedication error. Any two of those criteria may also give rise to thehighest likelihood of a LASA medication error, if both LASA drug pairmembers arc of the same dosage form that is not an oral solid. An oralsolid dosage form may be considered a likelihood diminishing factorbecause oral solids are more readily distinguished from each other thanare other dosage forms. In addition, other factors that may enhance ordiminish the likelihood of a LASA medication error may be identifiedthrough statistical analysis of historical prescription claimtransactions which involved actual LASA medication errors or falsedetections thereof.

Accordingly, Likelihood Indicators may be determined for all drugproducts associated with active LASA drug pairs, based on similarity ofthe drug names, prescribing frequencies, availability in same,look-alike or sound-alike strengths and other characteristics of theLASA drug pair members. Multiple Likelihood indicators may be determinedfor a drug product if that drug product is associated with multiple LASAdrug pairs. Likelihood Messages generated based on Likelihood Indicatorsmay indicate the LASA alternative drug names with which there is alikelihood of a LASA medication error. Multiple LASA alternative drugnames may potentially be included in a Likelihood Message. In certainembodiment, if at least one Likelihood Indicator is equal to aparticular value or values (e.g., 1 or 2), every LASA alternative drugname may be inserted into the Likelihood Message and may be orderedaccording to clinical significance of the associated LASA drug pairs.

The exemplary Likelihood Screening process 220 begins at starting block501 and progresses to step 502, where all active LASA drug pairassociated with the submitted drug product are selected. As mentionedpreviously, a list or table of LASA drug pairs may be stored in adatabase 105. A system administrator, such as a pharmacy manager, mayspecify which of the LASA drug pairs is the be considered “active” andtherefore examined during the Likelihood Screening process 220. It isassumed, based on the flow of FIG. 2, that the submitted drug productbelongs to at least one active LASA drug pair; if not, the LikelihoodScreening process 220 would not be performed. However, those skilled inthe art will appreciate that the Likelihood Screening process 220 may beused outside the context of FIG. 2 and could thus be modified to includea database check for at least one active LASA drug pair associated withthe submitted drug product.

Next at step 504, the database 105 is queried to determine theLikelihood Indicator(s) for the submitted drug product in relation toeach of the selected LASA drug pairs. The method next moves to step 506,where a determination is made as to whether the prescription claimrelates to a new prescription. If the prescription claim relates to anew prescription, the method proceeds to step 508 where the LikelihoodMessage and Likelihood Edit Action are determined for a newprescription, based on the Likelihood Indicators. If the prescriptionclaim does not relate to a new prescription, the method proceeds to step510 where the Likelihood Message and Likelihood Edit Action aredetermined for a refill prescription, based on the LikelihoodIndicators. To determine applicable Likelihood Messages and LikelihoodEdit Actions, a look-up table or other data structure containing suchinformation may be consulted. An exemplary look-up table is illustratedbelow as Table 4.

TABLE 4 Likelihood Messages and Edit Actions Likelihood Indicator New RxRefill Rx Likelihood Message: 1 Edit Action = Edit Action = “LASADrug-[Drug Name Reject Reject of Submitted Drug Product] looks/soundslike [LASA Alternative Drug Name(s)]” 2 Edit Action = Edit Action =“LASA Drug-[Drug Name Reject Capture of Submitted Drug Product]looks/sounds like [LASA Alternative Drug Name(s)]” 3 Edit Action = EditAction = “LASA Drug-[Drug Name Capture Capture of Submitted DrugProduct] looks/sounds like [LASA Alternative Drug Name(s)]” 4 EditAction = Edit Action = “LASA Drug-[Drug Name Capture None of SubmittedDrug Product] looks/sounds like [LASA Alternative Drug Name(s)]” 5 EditAction = Edit Action = “LASA Drug-[Drug Name None None of Submitted DrugProduct] looks/sounds like [LASA Alternative Drug Name(s)]”

As shown in Table 4, it may be administratively determined that anyprescription claim transaction involving a submitted drug product havingat least one Likelihood Indicator of I should be rejected, whether theprescription claim relates to a new prescription claim or a refill.Similarly, any prescription claim transaction involving a submitted drugproduct having at least one Likelihood Indicator of 2 should be rejectedif the prescription claim relates to a new prescription claim, butshould only be captured (recorded) in the case of a refill. As mentionedpreviously, Likelihood Edit Action parameters may be modified by asystem administrator. Again, in certain embodiment, if any LikelihoodIndicator for a submitted drug product corresponds to a RejectLikelihood Edit Action, all LASA alternative drug names may be insertedinto the Likelihood Message.

Likelihood Messages may be used to alert a pharmacist that a submitteddrug product has a drug name that looks and/or sounds like a LASAalternative drug name, in cases where confusion between the two drugnames is likely. In other embodiments, Likelihood Messages may provideother details besides the LASA alternative drug names. For example,Likelihood Messages may indicate whether the drug products associatedwith the LASA drug pair are newly available, which may serve as extraincentive to the pharmacists to double-check the drug name of thesubmitted drug product. After determining a Likelihood Message andLikelihood Edit Action for the selected LASA drug pair at either step508 or step 510, the method ends at step 512.

FIG. 6 is a flow chart illustrating an exemplary Reject Message buildand delivery process 222 (from FIG. 2) in accordance with certainembodiments of the present invention. The method begins at start block601, where it is assumed that all screening processes have beencompleted and any Absolute Dose Message and Absolute Dose Edit Action,Typical Dose Messages and Typical Dose Edit Actions and LikelihoodMessages and Likelihood Edit Actions have been identified. As previouslymentioned, a Reject Message is preferably built only if one or moreReject Edit Actions were identified by the various screening processes.Furthermore, if a Reject Message is built, it may include all screeningmessages (Absolute Dose Message, Typical Dose Message and/or LikelihoodMessages) associated with Reject Edit Actions, or only a subset thereof.The exemplary Reject Message build and delivery process 222 includesonly a subset of the screening messages in a Reject Message, so as toavoid including redundant information and to account for text spacelimitations.

Thus, at step 602 a determination is made as to whether an Absolute DoseEdit Action of Reject was identified by the Absolute Dose Screeningprocess. If so, the method moves to step 604, where a determination ismade as to whether a Typical Dose Edit Action of Reject was generated bythe Typical Dose Screening process. If a Typical Dose Edit Action ofReject was generated by the Typical Dose Screening process, the methodadvances to step 606, where a Reject Message is built and populated withthe Absolute Dose Message and the Typical Dose Message (if sufficienttext space is available). The Reject Message built at step 606 does notinclude any Likelihood Message(s), even if one or more Likelihood EditActions of Reject was identified. After building a Reject Message atstep 606, the Reject Message is delivered to the pharmacist (e.g., tothe pharmacy POS device 102) at step 608 and the method ends at step624.

However, if it is determined at step 604 that no Typical Dose EditAction of Reject was generated by the Typical Dose Screening process,the method advances to step 610 where a determination is made as towhether a Likelihood Edit Action of Reject was generated by theLikelihood Screening process. If it is determined at step 610 that aLikelihood Edit Action of Reject was generated, a Reject Message isbuilt and populated with the Absolute Dose Message and the LikelihoodMessage(s) (as text space permits) at step 612. If it is determined atstep 610 that no Likelihood Edit Action of Reject was generated, aReject Message is built and populated with only the Absolute DoseMessage at step 614. After building a Reject Message at step 612 or step614, the Reject Message is delivered to the pharmacist at step 608 andthe method ends at step 624.

Returning to step 602, if it is determined that no Absolute Dose EditAction of Reject was identified by the Absolute Dose Screening process,the method moves to step 616, where a determination is made as towhether a Typical Dose Edit Action of Reject was generated by theTypical Dose Screening process. If a Typical Dose Edit Action of Rejectwas generated by the Typical Dose Screening process, the method advancesto step 618, where a Reject Message is built and populated with only theTypical Dose Message. The Reject Message built at step 618 does notinclude any Likelihood Message(s), even if one or more Likelihood EditActions of Reject was identified. After building a Reject Message atstep 618, the Reject Message is delivered to the pharmacist (e.g., tothe pharmacy POS device 102) at step 608 and the method ends at step624.

However, if it is determined at step 616 that no Typical Dose EditAction of Reject was generated by the Typical Dose Screening process,the method advances to step 620 where a determination is made as towhether a Likelihood Edit Action of Reject was generated by theLikelihood Screening process. If it is determined at step 620 that aLikelihood Edit Action of Reject was generated, a Reject Message isbuilt and populated with only the Likelihood Message at step 622. Afterbuilding a Reject Message at step 622, the Reject Message is deliveredto the pharmacist at step 608 and the method ends at step 624. If it isdetermined at step 620 that no Likelihood Edit Action of Reject wasgenerated, the method ends at step 624 without building a RejectMessage.

As may be seen from the foregoing, the present invention providessystems and methods for analyzing prescription claim transactions inorder to identify potential LASA medication errors. Any one or more ofthe above described screening processes, or other screening processes,may be used to identify potential LASA medication errors. If potentialLASA medication errors are identified, appropriate Reject Messages maybe transmitted to the pharmacist. Potential LASA medication errors mayalso be recorded for subsequent analysis and reporting.

It should be appreciated that the exemplary aspects and features of thepresent invention as described above are not intended to be interpretedas required or essential elements of the invention, unless explicitlystated as such. It should also be appreciated that the foregoingdescription of exemplary embodiments was provided by way of illustrationonly and that many other modifications, features, embodiments andoperating environments are possible. For example, the present inventionis not intended to be limited to the prescription claim editingenvironment. In other embodiments, one or more of the LASA medicationerror screening processes can be readily adapted for application inelectronic prescription systems, hospital inpatient medication orderingsystems, etc.

In still other embodiments, a number of enhancements may be provided forimproving the sensitivity and specificity of the above-described LASAmedication error screening processes. By way of illustration, theTypical Dose Screening process may be modified to include age-tieredtypical dosing criteria, physician specialty-specific typical dosingcriteria, gender-specific typical dosing criteria, disease-specifictypical dosing criteria, and the like.

As another example, the Absolute Dose Screening and Typical DoseScreening processes may be adapted for use in connection with allprescription claim transactions, not just those involving a member of aLASA drug pair. Such an adaptation to the Typical Dose Screening processwould allow a pharmacist to be informed, for any drug product, when itis determined that the submitted daily dosage is highly unusual for thepatient's age and/or sex, even though it might satisfy absolute maximumand minimum dosing criteria. Such a system could also provide warningsthat a particular drug product is never used in a given patientpopulation or that it is highly unusual for a physician of a particularspecialty to be prescribing a particular drug product. The Typical DoseScreening process could also be adapted to account for criteria inaddition to typical dose criteria, such as typicalingredient(s)/strength(s)/dosage form combinations for patient groupand/or typical days supply.

In other embodiments, pharmacists may be provided with context-sensitivemessages regarding Continuing Education programs. The context sensitivemessages may be included in Reject Messages or in separately deliveredtransmissions, such as email messages or facsimiles. Based onsponsor-identified triggers, individual pharmacists can be directed toappropriate drug-focused or disease-focused Continuing Educationprograms and may be informed as to the trigger event that resulted inthe invitation. Trigger events could include having a particular drug ordrug class exceed a preset threshold in terms of percent of totalprescriptions, or number of prescription dispensed in a week or month. Atrigger event could also include dispensing the first prescription for abrand new drug. Or, for very rare but complex drugs or diseases, atrigger event may occur every time a related new prescription isdispensed. These and other trigger events will occur to those ofordinary skill in the art.

Therefore, it is contemplated that any and all such embodiments areincluded in the present invention as may fall within the literal orequivalent scope of the appended claims. The scope of the presentinvention is to be limited only by the following claims and not by theforegoing description of exemplary and alternative embodiments.

1. A method for look-alike sound-alike medication error messaging,comprising: calculating, by a host server, a set of most common dailydosage (MCDD) values or a set of common daily dosage (CDD) values foreach of a plurality of drug products, based at least in part on dosagevalues from previously received prescription claim transactions receivedby the host server from a plurality of pharmacy point of service (POS)devices; storing, in a database accessible by the host server, the MCDDor CDD value sets for each of the plurality of drug products; receiving,at the host server, a prescription claim transaction from a pharmacy POSdevice; determining, from the prescription claim transaction received atthe host server, a submitted drug product and a submitted daily dosagefor the submitted drug product, wherein the submitted drug product isone of the plurality of drug products stored in the database;determining, by the host server, that the submitted drug product isassociated with at least one look-alike sound-alike alternative drugproduct; determining, by the host server, that the submitted dailydosage is not within the MCDD or CDD value sets for the submitted drugproduct based on a comparison of the submitted daily dosage to the MCDDor CDD value sets for the submitted drug product; determining, by thehost server, that the submitted daily dosage is within the MCDD or CDDvalue sets for the at least one look-alike sound-alike alternative drugproduct based on a comparison of the submitted daily dosage to the MCDDor CDD value sets for the at least one look-alike sound-alikealternative drug product; determining that the prescription claimtransaction should be rejected based at least in part on thedetermination that the submitted daily dosage is within the MCDD or CDDvalue sets for the at least one look-alike sound-alike alternative drugproduct; generating a reject message at the host server, wherein thereject message indicates a potential prescription error has beendetected; and transmitting the reject message from the host server tothe pharmacy POS device.
 2. The method of claim 1, wherein determiningthat the prescription claim transaction should be rejected includesdetermining a likelihood indicator, wherein the likelihood indicatorrepresents a relative probability of whether the submitted drug productis involved in a look-alike sound-alike medication error involving theat least one look-alike sound-alike alternative drug product.
 3. Themethod of claim 2, wherein the likelihood indicator is determined basedon a degree of similarity between drug names of the submitted drugproduct and the at least one look-alike sound-alike alternative drugproduct, prescribing frequencies of the submitted drug product and theat least one look-alike sound-alike alternative drug product, andwhether the submitted drug product and the at least one look-alikesound-alike alternative drug product are available in similar strengths.4. The method of claim 3, wherein the prescribing frequencies of thesubmitted drug product and the at least one look-alike sound-alikealternative drug product are categorized as being either high, medium orlow; and wherein a low-low, high-low or low-high combination ofprescribing frequencies is considered to have the potential forconfirmation bias.
 5. The method of claim 1, wherein determining thatthe prescription claim transaction should be rejected includesdetermining whether the submitted daily dosage meets absolute dosingcriteria for the submitted drug product.
 6. The method of claim 5,wherein the absolute dosing criteria is specific to at least one of thegroup consisting of: patient demographic group, treatment type andillness type.
 7. The method of claim 5, wherein the submitted dailydosage is determined to not meet the absolute dosing criteria for thesubmitted drug product because the submitted daily dosage is lower thanan absolute minimum daily dosage for the submitted drug product.
 8. Themethod of claim 5, wherein the submitted daily dosage is determined tonot meet the absolute dosing criteria for the submitted drug productbecause the submitted daily dosage exceeds an absolute maximum dailydosage for the submitted drug product.
 9. The method of claim 1, whereinthe CDD value set for each of a plurality of drug products is within adaily dosage range above a predefined percentage of all dosage values ofthe previously received prescription claim transactions associated withthe submitted drug product.
 10. The method of claim 9, wherein the MCDDvalue set for a particular drug product is within a daily dosage rangeabove a predefined percentage of all dosage values of the previouslyreceived prescription claim transactions associated with the submitteddrug product, the percentage being larger and the range being narrowerthan that used to determine the CDD value set for that particular drugproduct.
 11. The method of claim 1, wherein determining that thesubmitted daily dosage is not within the MCDD or CDD value sets for thesubmitted drug product includes determining that the submitted dailydosage is not within a deviation of at least one value of the MCDD orCDD value sets.
 12. The method of claim 11, wherein each of the MCDD orCDD value sets includes one or more values.
 13. The method of claim 1,wherein determining that the prescription claim transaction should berejected includes determining whether the prescription relates to thenew prescription or a refill.
 14. The method of claim 1, whereindetermining that the prescription claim transaction should be rejectedincludes determining a clinical significance for the look-alikesound-alike drug pair, the clinical significance being a value used toquantify the consequences of a look-alike sound-alike medication errorinvolving the look-alike sound-alike drug pair.
 15. The method of claim1, wherein each of the MCDD or CDD value sets is specific to at leastone of the group consisting of: patient demographic group, treatmenttype, illness type and physician specialty.
 16. The method of claim 1,wherein the reject message identifies the at least one look-alikesound-alike alternative drug product.
 17. A system for look-alikesound-alike medication error messaging, comprising: a network interface;a database; and a processor, located at a host server, in communicationwith the network interface and the database, wherein the processor isconfigured to: calculate a set of most common daily dosage (MCDD) valuesor a set of common daily dosage (CDD) values for each of a plurality ofdrug products, based at least in part on dosage values from previouslyreceived prescription claim transactions received by the host serverfrom a plurality of pharmacy point of service (POS) devices; store, inthe database, the MCDD or CDD value sets for each of the plurality ofdrug products; receive, via the network interface, a prescription claimtransaction from a pharmacy point of service (POS) device; determine,from the prescription claim transaction, a submitted drug product and asubmitted daily dosage for the submitted drug product, wherein thesubmitted drug product is one of the plurality of drug products storedin the database; determine that the submitted drug product is associatedwith at least one look-alike sound-alike alternative drug product;determine that the submitted daily dosage is not within the MCDD or CDDvalue sets for the submitted drug product based on a comparison of thesubmitted daily dosage to the MCDD or CDD value sets for the submitteddrug product; determine that the submitted daily dosage is within theMCDD or CDD value sets for the at least one look-alike sound-alikealternative drug product based on a comparison of the submitted dailydosage to the MCDD or CDD value sets for the at least one look-alikesound-alike alternative drug product; determine that the prescriptionclaim transaction should be rejected based at least in part on thedetermination that the submitted daily dosage is within the MCDD or CDDvalue sets for the at least one look-alike sound-alike alternative drugproduct; generate a reject message, wherein the reject message indicatesa potential prescription error has been detected; and transmit thereject message from the host server to the pharmacy POS device.
 18. Thesystem of claim 17, wherein the computer-executable instructions todetermine that the prescription claim transaction should be rejectedinclude computer-executable instructions to determine a likelihoodindicator, wherein the likelihood indicator represents a relativeprobability of whether the submitted drug product is involved in alook-alike sound-alike medication error involving the at least onelook-alike sound-alike alternative drug product.
 19. The system of claim18, wherein the likelihood indicator is determined based on a degree ofsimilarity between drug names of the submitted drug product and the atleast one look-alike sound-alike alternative drug product, prescribingfrequencies of the submitted drug product and the at least onelook-alike sound-alike alternative drug product, and whether thesubmitted drug product and the at least one look-alike sound-alikealternative drug product are available in similar strengths.
 20. Thesystem of claim 19, wherein the prescribing frequencies of the submitteddrug product and the at least one look-alike sound-alike alternativedrug product are categorized as being either high, medium or low; andwherein a low-low, high-low or low-high combination of prescribingfrequencies is considered to have the potential for confirmation bias.21. The system of claim 17, wherein the computer-executable instructionsto determine that the prescription claim transaction should be rejectedinclude computer-executable instructions to determine whether thesubmitted daily dosage meets absolute dosing criteria for the submitteddrug product.
 22. The system of claim 21, wherein the absolute dosingcriteria are specific to at least one of the group consisting of:patient demographic group, treatment type and illness type.
 23. Thesystem of claim 21, wherein the submitted daily dosage is determined tonot meet the absolute dosing criteria for the submitted drug productbecause the submitted daily dosage is lower than an absolute minimumdaily dosage for the submitted drug product.
 24. The system of claim 21,wherein the submitted daily dosage is determined to not meet theabsolute dosing criteria for the submitted drug product because thesubmitted daily dosage exceeds an absolute maximum daily dosage for thesubmitted drug product.
 25. The system of claim 17, wherein the CDDvalue set for each of a plurality of drug products is within a dailydosage range above a predefined percentage of all dosage values of thepreviously received prescription claim transactions associated with thesubmitted drug product.
 26. The system of claim 25, wherein the MCDDvalue set for a particular drug product is within a daily dosage rangeabove a predefined percentage of all dosage values of the previouslyreceived prescription claim transactions associated with the submitteddrug product, the percentage being larger and the range being narrowerthan that used to determine the CDD value set for that particular drugproduct.
 27. The system of claim 17, wherein determining that thesubmitted daily dosage is within the MCDD or CDD value sets for the atleast one look-alike sound-alike alternative drug product includesdetermining that the submitted daily dosage is within a deviation of atleast one value of the MCDD or CDD value sets.
 28. The system of claim27, wherein each of the MCDD or CDD value sets includes one or morevalues.
 29. The system of claim 17, wherein the computer-executableinstructions to determine that the prescription claim transaction shouldbe rejected include computer-executable instructions to determinewhether the prescription relates to the new prescription or a refill.30. The system of claim 17, wherein the computer-executable instructionsto determine that the prescription claim transaction should be rejectedinclude computer-executable instructions to determine a clinicalsignificance for the look-alike sound-alike drug pair, the clinicalsignificance being a value used to quantify the consequences of alook-alike sound-alike medication error involving the look-alikesound-alike drug pair.
 31. The system of claim 17, wherein each of theMCDD or CDD value sets is specific to at least one of the groupconsisting of: patient demographic group, treatment type, illness typeand physician specialty.
 32. The system of claim 17, wherein the rejectmessage identifies the at least one look-alike sound-alike alternativedrug product.